Protein structure-based drug design: from docking to molecular dynamics

Śledź, P.; Caflisch, Amedeo

doi:10.1016/j.sbi.2017.10.010

Cited by 471 publications

(241 citation statements)

References 84 publications

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“…[56][57][58][59][60][61] Still, it remains a major challenge to account for protein dynamics in structure-based drug-design. 62 This library of Affimers allows exploration of a dynamic range of protein target conformers, potentially facilitating generation of template pharmacophores for small-molecule ligand design and structure based-ligand design, which may offer an advantage over the current process that typically operates using static crystal structures. 62 In summary, we have identified non-natural protein ligands that exhibit selectivity for different BCL-2 family members.…”

Section: Discussion (Without Subheadings)mentioning

confidence: 99%

Selective Affimers Recognize BCL-2 Family Proteins Through Non-Canonical Structural Motifs

Miles¹,

Hóbor²,

Taylor³

et al. 2019

Preprint

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no more than 200 words)The BCL-2 family is a challenging set of proteins to target selectively due to sequence and structural homologies across the family. Selective ligands for the BCL-2 family regulators of apoptosis are desirable as probes to understand cell biology and apoptotic signalling pathways, and as starting points for inhibitor design. We have used phage display to isolate Affimer reagents (non-antibody binding proteins based on a conserved scaffold) to identify ligands for MCL-1, BCL-xL, BCL-2, BAK and BAX, then used multiple biophysical characterisation methods to probe the interactions. We established that purified Affimers elicit selective and potent recognition of their target BCL-2 protein. For anti-apoptotic targets, competitive inhibition of their canonical protein-protein interactions is demonstrated. Cocrystal structures reveal an unprecedented mode of molecular recognition; where a BH3 helix is normally bound, flexible loops from the Affimer dock into the BH3 binding cleft. Moreover, the Affimers induce a change in the target proteins towards a desirable drug bound like conformation. These results indicate Affimers can be used as alternative templates to inspire design of selective BCL-2 family modulators, and provide proof-ofconcept for the elaboration of selective non-antibody binding reagents for use in cell-biology applications. [198 words]

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Section: Discussion (Without Subheadings)mentioning

confidence: 99%

Selective Affimers Recognize BCL-2 Family Proteins Through Non-Canonical Structural Motifs

Miles¹,

Hóbor²,

Taylor³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Many previous studies pointed out that docking alone was not adequate to accurately discern the interactions of ligands and proteins that have high flexibility and similarity (Carlsson, Boukharta, & Aqvist, 2008;Chohan, Qian, Pan, & Chen, 2016;Li et al, 2009). However, docking combined with MD simulations was a good strategy to address this issue and has been widely applied in many systems (Alonso, Bliznyuk, & Gready, 2006;Sledz & Caflisch, 2018). Therefore, the two complexes were subsequently subjected to 250 ns MD simulations.…”

Section: Complex Models Of Cyp3a4/cyp3a5 With Se By Dockingmentioning

confidence: 99%

Computational insights into the different catalytic activities of CYP3A4 and CYP3A5 toward schisantherin E

Xue

et al. 2019

Chem Biol Drug Des

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The cytochromes CYP3A4 and CYP3A5 share 84% sequence identity, but they exhibit different catalytic activities toward some substrates. Schisantherin E (SE) was recently identified as a selective substrate of CYP3A5, which exhibited catalytic efficiency that was more than 23 times higher than CYP3A4. At present, however, the structural determinants responsible for the different catalytic activities of the two enzymes toward SE have not been fully understood. In this study, a combination of molecular docking, molecular dynamic simulations, and binding free energy calculation was performed on the CYP3A4/CYP3A5‐SE systems to investigate the issue. The results demonstrate that Ser119 in CYP3A4 and Glu374 in CYP3A5 formed direct hydrogen bonding with SE, respectively. Additionally, one water molecule located between the B‐C loop and the I helix mediated different hydrogen‐bonding networks between CYP3A4/3A5 and SE. The residue differences (Phe/Leu108 and Leu/Phe210) triggered the distinct conformational changes of the Phe‐cluster residues, especially Phe213 and Phe215, which formed stronger hydrophobic interactions with SE in CYP3A5. The calculated binding free energies were consistent with the experimental results.

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“…Ligand docking is one of the most widely applied computational approaches in drug discovery . Modern docking algorithms and scoring functions are powerful tools for predicting the likely binding pose of small molecules .…”

Section: Introductionmentioning

confidence: 99%

ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance

Fan

Bauer

Stork

et al. 2019

Molecular Informatics

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Protein flexibility and solvation pose major challenges to docking algorithms and scoring functions. One established strategy for addressing these challenges is to use multiple protein conformations for docking (all‐against‐all ensemble docking). Recent studies have shown that the performance of ensemble docking can be improved by selecting the most relevant protein structures for docking. In search for a robust approach to protein structure selection, we have come up with an integrated mAchine Learning AnD DockINg approach (ALADDIN). ALADDIN employs a battery of random forest classifiers to select, individually for each compound of interest, from an ensemble of protein structures, the single most suitable protein structure for docking. ALADDIN outperformed the best single‐structure docking runs, ensemble docking and a similarity‐based docking approach on three out of four investigated targets, with up to 0.15, 0.11 and 0.16 higher area under the receiver operating characteristic curve (AUC) values, respectively. Only in the case of cytochrome P450 3A4, ALADDIN, like any of the other tested approaches, failed to obtain decent performance. ALADDIN can be particularly useful for structure‐based virtual screening of malleable proteins, including kinases, some viral enzymes and anti‐targets.

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Protein structure-based drug design: from docking to molecular dynamics

Cited by 471 publications

References 84 publications

Selective Affimers Recognize BCL-2 Family Proteins Through Non-Canonical Structural Motifs

Selective Affimers Recognize BCL-2 Family Proteins Through Non-Canonical Structural Motifs

Computational insights into the different catalytic activities of CYP3A4 and CYP3A5 toward schisantherin E

ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance

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